{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "X = X[y<2,:2]\n",
    "y = y[y<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0],X[y==0,1], color='red')\n",
    "plt.scatter(X[y==1,0],X[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "standardScaler = StandardScaler()\n",
    "standardScaler.fit(X)\n",
    "X_standard = standardScaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1000000000.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svc = LinearSVC(C=1e9)\n",
    "svc.fit(X_standard, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1,1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1,1)\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "\n",
    "    plt.contourf(x0, x1, zz, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-3,3,-3,3])\n",
    "plt.scatter(X_standard[y==0,0],X_standard[y==0,1], color='red')\n",
    "plt.scatter(X_standard[y==1,0],X_standard[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.01, class_weight=None, dual=True, fit_intercept=True,\n",
       "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc2 = LinearSVC(C=0.01)\n",
    "svc2.fit(X_standard, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc2, axis=[-3,3,-3,3])\n",
    "plt.scatter(X_standard[y==0,0],X_standard[y==0,1], color='red')\n",
    "plt.scatter(X_standard[y==1,0],X_standard[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.03240038, -2.50701084]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92736326])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc_decision_boundary(model, axis):\n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1,1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1,1)\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "\n",
    "    plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
    "    \n",
    "    w = model.coef_[0]\n",
    "    b = model.intercept_[0]\n",
    "    \n",
    "    # w0 * x0 + x1 * x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    plot_x = np.linspace(axis[0], axis[1], 200)\n",
    "    up_y = -w[0]/w[1] * plot_x - b/w[1] + 1/w[1]\n",
    "    down_y = -w[0]/w[1] * plot_x - b/w[1] - 1/w[1]\n",
    "    \n",
    "    up_index = (up_y >= axis[2]) & (up_y <= axis[3])\n",
    "    down_index = (down_y >= axis[2]) & (down_y <= axis[3])\n",
    "    plt.plot(plot_x[up_index], up_y[up_index], color='black')\n",
    "    plt.plot(plot_x[down_index], down_y[down_index], color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_decision_boundary(svc, axis=[-3,3,-3,3])\n",
    "plt.scatter(X_standard[y==0,0],X_standard[y==0,1], color='red')\n",
    "plt.scatter(X_standard[y==1,0],X_standard[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_decision_boundary(svc2, axis=[-3,3,-3,3])\n",
    "plt.scatter(X_standard[y==0,0],X_standard[y==0,1], color='red')\n",
    "plt.scatter(X_standard[y==1,0],X_standard[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.43789811, -0.41091944]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_decision_boundary' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-bde52c399831>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplot_decision_boundary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoly_svc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_standard\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX_standard\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_standard\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX_standard\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'blue'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plot_decision_boundary' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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